SHOW_ALL_RESULTS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "pd.set_option('display.max_rows', 30)\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "def print_value(val):\n",
    "    if val == 1: return \"POSITIVE\"\n",
    "    if val == -1: return \"NEGATIVE\"\n",
    "    if val == 0: return \"NEUTRAL\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading CSV: 129274 tweet found [ 1046 users]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>lang</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46820</th>\n",
       "      <td>BPH83</td>\n",
       "      <td>Watkins taps one in again but then we all realise he has a testicle offside https://t.co/jbgw1gUpZm</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>hasanaytekin</td>\n",
       "      <td>- Elektrikli otomobilin bakım maliyeti düşüktür\\n- Elektrikli otomobilin pili ağır ve aracın ortasında konumlandığı için araç sürüşü daha iyidir\\n- Elektrik motoru içten yanmalı motorlardan çok daha basit ve hafiftir\\n- Elektrik motorunun güç aktarımı çok hızlıdır \\nDevam edecek https://t.co/HkK20nhC9m</td>\n",
       "      <td>tr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54326</th>\n",
       "      <td>elizmase</td>\n",
       "      <td>Excited to stock up on treats for our little Halloween fest tonight - i.e. sweets and wine!</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41123</th>\n",
       "      <td>mark_mark1media</td>\n",
       "      <td>They Lied and people died!!!!! https://t.co/6RnvvbFAZY</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104878</th>\n",
       "      <td>AGnomblea</td>\n",
       "      <td>Vous souvenez-vous quand vous avez rejoint Twitter ? Moi, oui ! #MonAnniversaireTwitter https://t.co/s1VpVjmxhJ</td>\n",
       "      <td>fr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33329</th>\n",
       "      <td>hiilog</td>\n",
       "      <td>#docあすへのカルテ ここから崩壊するのか。辛い</td>\n",
       "      <td>ja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77431</th>\n",
       "      <td>Calabria_7</td>\n",
       "      <td>Premio a questore #Macerata: \"A chi ha perso la vita per il nostro Paese\" | Calabria7\\nLeggi la #notizia⤵️ https://t.co/vSjzWSgYWW</td>\n",
       "      <td>it</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60655</th>\n",
       "      <td>jcrawf74</td>\n",
       "      <td>My 40th birthday cake. Belter! #Fingers @wickbear26 @RigidDigits @JakeBurnsSLF @gazslf @sonicanne1 http://t.co/gNAqjWVkjp</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110469</th>\n",
       "      <td>Workbooks</td>\n",
       "      <td>Since switching to Workbooks, Eilersen has been able to switch processes and sustain growth.\\n\\nFind out why they chose us over Salesforce.\\n\\n#CRMSuccess #sme #workbooks\\n\\nhttps://t.co/XuqPbCpEn0 https://t.co/aXTdVhVQ98</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102004</th>\n",
       "      <td>CharliCass</td>\n",
       "      <td>Why do these girls drag there words so muuuuuuuuch #LoveIsland</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129274 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   User  \\\n",
       "46820             BPH83   \n",
       "1032       hasanaytekin   \n",
       "54326          elizmase   \n",
       "41123   mark_mark1media   \n",
       "104878        AGnomblea   \n",
       "...                 ...   \n",
       "33329            hiilog   \n",
       "77431        Calabria_7   \n",
       "60655          jcrawf74   \n",
       "110469        Workbooks   \n",
       "102004       CharliCass   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                  Tweet  \\\n",
       "46820                                                                                                                                                                                                               Watkins taps one in again but then we all realise he has a testicle offside https://t.co/jbgw1gUpZm   \n",
       "1032    - Elektrikli otomobilin bakım maliyeti düşüktür\\n- Elektrikli otomobilin pili ağır ve aracın ortasında konumlandığı için araç sürüşü daha iyidir\\n- Elektrik motoru içten yanmalı motorlardan çok daha basit ve hafiftir\\n- Elektrik motorunun güç aktarımı çok hızlıdır \\nDevam edecek https://t.co/HkK20nhC9m   \n",
       "54326                                                                                                                                                                                                                       Excited to stock up on treats for our little Halloween fest tonight - i.e. sweets and wine!   \n",
       "41123                                                                                                                                                                                                                                                            They Lied and people died!!!!! https://t.co/6RnvvbFAZY   \n",
       "104878                                                                                                                                                                                                  Vous souvenez-vous quand vous avez rejoint Twitter ? Moi, oui ! #MonAnniversaireTwitter https://t.co/s1VpVjmxhJ   \n",
       "...                                                                                                                                                                                                                                                                                                                 ...   \n",
       "33329                                                                                                                                                                                                                                                                                         #docあすへのカルテ ここから崩壊するのか。辛い   \n",
       "77431                                                                                                                                                                                Premio a questore #Macerata: \"A chi ha perso la vita per il nostro Paese\" | Calabria7\\nLeggi la #notizia⤵️ https://t.co/vSjzWSgYWW   \n",
       "60655                                                                                                                                                                                         My 40th birthday cake. Belter! #Fingers @wickbear26 @RigidDigits @JakeBurnsSLF @gazslf @sonicanne1 http://t.co/gNAqjWVkjp   \n",
       "110469                                                                                    Since switching to Workbooks, Eilersen has been able to switch processes and sustain growth.\\n\\nFind out why they chose us over Salesforce.\\n\\n#CRMSuccess #sme #workbooks\\n\\nhttps://t.co/XuqPbCpEn0 https://t.co/aXTdVhVQ98   \n",
       "102004                                                                                                                                                                                                                                                   Why do these girls drag there words so muuuuuuuuch #LoveIsland   \n",
       "\n",
       "       lang  \n",
       "46820    en  \n",
       "1032     tr  \n",
       "54326    en  \n",
       "41123    en  \n",
       "104878   fr  \n",
       "...     ...  \n",
       "33329    ja  \n",
       "77431    it  \n",
       "60655    en  \n",
       "110469   en  \n",
       "102004   en  \n",
       "\n",
       "[129274 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mostra tweets originali\n",
    "twitter_dataframe = pd.read_csv('dataframes/tweets.csv')\n",
    "all_user_list = list(np.unique(twitter_dataframe[\"User\"])) #lista di utenti\n",
    "print(\"loading CSV: \"+str(twitter_dataframe.shape[0])+\" tweet found [\",len(all_user_list),\"users]\")\n",
    "shuffle(twitter_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading CSV: 119524 tweet found [ 1018 users]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76511</th>\n",
       "      <td>donnamarie2607</td>\n",
       "      <td>What is going on with @ITV Hub on @NOWTV It's not updating and I want to watch @emmerdale 😠😠😠</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44934</th>\n",
       "      <td>MonicaBulanday</td>\n",
       "      <td>Shop @soundcoreaudio #affiliate #sponsored #ad #brandambassador #soundcoreXmonicabulanday #soundcore #soundcoreaudio #monicabulandayaffiliates #monicabulandaypaidpartnerships #QueenOfAffiliatesAndPaidPartnerships #Philippines https://t.co/dcEOXZBlmQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33506</th>\n",
       "      <td>Kavenerocaffe</td>\n",
       "      <td>Do you want to taste the real taste of coffee? Our high quality capsules offer you a unique and intense experience. Seeing is believing! #coffee #capsules #quality</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65091</th>\n",
       "      <td>_Lexworth_</td>\n",
       "      <td>I am using this one:   http://t.co/6Kgb6N4u\\n via Retina Wallpapers HD, http://t.co/kLpM9Pg3 @appwill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39653</th>\n",
       "      <td>MTDIXIT</td>\n",
       "      <td>YES I KNOW Pino Daniele. From an immense city like Naples, immense musicians have grown up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58762</th>\n",
       "      <td>TheGrocer</td>\n",
       "      <td>The Office for Health Improvement and Disparities today released the final report on the programme, more than a year later than promised\\nhttps://t.co/LpgNOdwA3D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108900</th>\n",
       "      <td>smithywannabe</td>\n",
       "      <td>Jonny rocks! Ha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107249</th>\n",
       "      <td>scottneedham33</td>\n",
       "      <td>500 reps in 30mins #StayHench or more appropriately #SkinnyWrench</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12598</th>\n",
       "      <td>CityBluebirds_</td>\n",
       "      <td>Mark Hudson gets it 👏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75992</th>\n",
       "      <td>debbie_fisayo</td>\n",
       "      <td>Believe in yourself that whatever happens, you gonna make it... I will never be poor, I refuse to be poor!\\nThink positive\\nLive positive\\nBe optimistic about yourself.\\n#livepositive\\n#positivity\\n#motivational\\n#entrepreneurs \\n#business https://t.co/WfvJ6hvTH2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119524 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  User  \\\n",
       "76511   donnamarie2607   \n",
       "44934   MonicaBulanday   \n",
       "33506    Kavenerocaffe   \n",
       "65091       _Lexworth_   \n",
       "39653          MTDIXIT   \n",
       "...                ...   \n",
       "58762        TheGrocer   \n",
       "108900   smithywannabe   \n",
       "107249  scottneedham33   \n",
       "12598   CityBluebirds_   \n",
       "75992    debbie_fisayo   \n",
       "\n",
       "                                                                                                                                                                                                                                                                          Tweet  \n",
       "76511                                                                                                                                                                             What is going on with @ITV Hub on @NOWTV It's not updating and I want to watch @emmerdale 😠😠😠  \n",
       "44934                 Shop @soundcoreaudio #affiliate #sponsored #ad #brandambassador #soundcoreXmonicabulanday #soundcore #soundcoreaudio #monicabulandayaffiliates #monicabulandaypaidpartnerships #QueenOfAffiliatesAndPaidPartnerships #Philippines https://t.co/dcEOXZBlmQ  \n",
       "33506                                                                                                       Do you want to taste the real taste of coffee? Our high quality capsules offer you a unique and intense experience. Seeing is believing! #coffee #capsules #quality  \n",
       "65091                                                                                                                                                                     I am using this one:   http://t.co/6Kgb6N4u\\n via Retina Wallpapers HD, http://t.co/kLpM9Pg3 @appwill  \n",
       "39653                                                                                                                                                                                YES I KNOW Pino Daniele. From an immense city like Naples, immense musicians have grown up  \n",
       "...                                                                                                                                                                                                                                                                         ...  \n",
       "58762                                                                                                         The Office for Health Improvement and Disparities today released the final report on the programme, more than a year later than promised\\nhttps://t.co/LpgNOdwA3D  \n",
       "108900                                                                                                                                                                                                                                                          Jonny rocks! Ha  \n",
       "107249                                                                                                                                                                                                        500 reps in 30mins #StayHench or more appropriately #SkinnyWrench  \n",
       "12598                                                                                                                                                                                                                                                     Mark Hudson gets it 👏  \n",
       "75992   Believe in yourself that whatever happens, you gonna make it... I will never be poor, I refuse to be poor!\\nThink positive\\nLive positive\\nBe optimistic about yourself.\\n#livepositive\\n#positivity\\n#motivational\\n#entrepreneurs \\n#business https://t.co/WfvJ6hvTH2  \n",
       "\n",
       "[119524 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mostra tweets (con traduzione)\n",
    "twitter_dataframe = pd.read_csv('dataframes/tweets_translated.csv')\n",
    "all_user_list = list(np.unique(twitter_dataframe[\"User\"])) #lista di utenti\n",
    "print(\"loading CSV: \"+str(twitter_dataframe.shape[0])+\" tweet found [\",len(all_user_list),\"users]\")\n",
    "shuffle(twitter_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading CSV: 12585 tweet found [ 498 users]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Tweet</th>\n",
       "      <th>Cleaned-tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2154</th>\n",
       "      <td>Emily_G_929</td>\n",
       "      <td>OMG @LZZYHALE THIS clothing IS SO MOTHERFUCKING ADORABLE AND GRUNGE 😭🤘🏻 https://t.co/7tWzv08lWm</td>\n",
       "      <td>omg this clothing be so motherfucking adorable and grunge loudly cry face sign of the horn light skin tone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8470</th>\n",
       "      <td>figlioderoma</td>\n",
       "      <td>He doesnt look a thing like jesus, but he talks like a gentleman like you remember when you were baby.</td>\n",
       "      <td>he do not look a thing like jesus but he talk like a gentleman like you remember when you be baby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4306</th>\n",
       "      <td>MMyrick29</td>\n",
       "      <td>#SeaKraken wins and THAT'S HOCKEY, BABY! @SeattleKraken 4, @BuffaloSabres 3. @emmitnosee04, @mwash90, @thedalaivala, @SeattleNative57, @0fficialStacey, @madisoncwade, @ErinMayovsky, @GaardSwanson &amp;amp; @CatherineInSea! #SEAvsBUF</td>\n",
       "      <td>sea kraken win and that be hockey baby! . ! sea v bu f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4774</th>\n",
       "      <td>MeritaSocial</td>\n",
       "      <td>The future of Italian #industry is in the #South where there are still unsaturated territories with qualified baby people increasingly oriented towards #business culture Interview with President Maurizio Manfellotto on #RecoveryPlan and reforms right arrow curving down</td>\n",
       "      <td>the future of italian industry be in the south where there be still unsaturated territory with qualified baby people increasingly orient towards business culture interview with president maurizio manfellotto on recovery plan and reform right arrow curve down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6774</th>\n",
       "      <td>YleniaNea</td>\n",
       "      <td>I have a very clear and sensitive complexion. I have always used protection since I was a baby. I also use it in the city of Turin. I would like to understand what the problem is with those who tell me but if you wear protection you will never get a tan</td>\n",
       "      <td>i have a very clear and sensitive complexion. i have always use protection since i be a baby. i also use it in the city of turin. i would like to understand what the problem be with those who tell me but if you wear protection you will never get a tan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>FoodBev</td>\n",
       "      <td>Vegetable producer @GreenGiant has launched three new category innovations for the freezer aisle: Restaurant fashion sides, Zucchini Tots and Veggie Spiral Skillets.\\n\\n#food https://t.co/JrGbLU8hiW</td>\n",
       "      <td>vegetable producer have launch three new category innovation for the freezer aisle restaurant fashion side zucchini tot and veggie spiral skillets. food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8554</th>\n",
       "      <td>fondpirelli</td>\n",
       "      <td>For #Museocity, create your map of Milan with us, a free interactive digital itinerary dedicated to baby between and years to get to know the places in the city linked to the history of . We look forward to seeing you on Saturday March at . left arrow curving right</td>\n",
       "      <td>for museo city create your map of milan with u a free interactive digital itinerary dedicate to baby between and year to get to know the place in the city link to the history of . we look forward to see you on saturday march at . left arrow curve right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11771</th>\n",
       "      <td>stevenmayatt</td>\n",
       "      <td>Silks at office Fly \\n#splits #studiofly #weheartpole #silks #behindthescenes https://t.co/UYmkrgjk3k</td>\n",
       "      <td>silk at office fly split studio fly we heart pole silk behind the scene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5003</th>\n",
       "      <td>MinaDiBari</td>\n",
       "      <td>wow\\n\\nThe Dick car Dyke Show - Season 2, Episode 17 - Will You Be My Wife? - F... https://t.co/fuSEfJoQcy via @YouTube</td>\n",
       "      <td>wow the dick car dyke show season episode will you be my wife? f. via</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8213</th>\n",
       "      <td>elizmase</td>\n",
       "      <td>Still haven't mastered how to combine the fact that I like to sit cross legged on my chair at work, and wearing a summer clothing #help</td>\n",
       "      <td>still have not master how to combine the fact that i like to sit cross legged on my chair at work and wear a summer clothing help</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12585 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               User  \\\n",
       "2154    Emily_G_929   \n",
       "8470   figlioderoma   \n",
       "4306      MMyrick29   \n",
       "4774   MeritaSocial   \n",
       "6774      YleniaNea   \n",
       "...             ...   \n",
       "2335        FoodBev   \n",
       "8554    fondpirelli   \n",
       "11771  stevenmayatt   \n",
       "5003     MinaDiBari   \n",
       "8213       elizmase   \n",
       "\n",
       "                                                                                                                                                                                                                                                                               Tweet  \\\n",
       "2154                                                                                                                                                                                 OMG @LZZYHALE THIS clothing IS SO MOTHERFUCKING ADORABLE AND GRUNGE 😭🤘🏻 https://t.co/7tWzv08lWm   \n",
       "8470                                                                                                                                                                          He doesnt look a thing like jesus, but he talks like a gentleman like you remember when you were baby.   \n",
       "4306                                            #SeaKraken wins and THAT'S HOCKEY, BABY! @SeattleKraken 4, @BuffaloSabres 3. @emmitnosee04, @mwash90, @thedalaivala, @SeattleNative57, @0fficialStacey, @madisoncwade, @ErinMayovsky, @GaardSwanson &amp; @CatherineInSea! #SEAvsBUF   \n",
       "4774   The future of Italian #industry is in the #South where there are still unsaturated territories with qualified baby people increasingly oriented towards #business culture Interview with President Maurizio Manfellotto on #RecoveryPlan and reforms right arrow curving down   \n",
       "6774                   I have a very clear and sensitive complexion. I have always used protection since I was a baby. I also use it in the city of Turin. I would like to understand what the problem is with those who tell me but if you wear protection you will never get a tan   \n",
       "...                                                                                                                                                                                                                                                                              ...   \n",
       "2335                                                                          Vegetable producer @GreenGiant has launched three new category innovations for the freezer aisle: Restaurant fashion sides, Zucchini Tots and Veggie Spiral Skillets.\\n\\n#food https://t.co/JrGbLU8hiW   \n",
       "8554       For #Museocity, create your map of Milan with us, a free interactive digital itinerary dedicated to baby between and years to get to know the places in the city linked to the history of . We look forward to seeing you on Saturday March at . left arrow curving right   \n",
       "11771                                                                                                                                                                          Silks at office Fly \\n#splits #studiofly #weheartpole #silks #behindthescenes https://t.co/UYmkrgjk3k   \n",
       "5003                                                                                                                                                         wow\\n\\nThe Dick car Dyke Show - Season 2, Episode 17 - Will You Be My Wife? - F... https://t.co/fuSEfJoQcy via @YouTube   \n",
       "8213                                                                                                                                         Still haven't mastered how to combine the fact that I like to sit cross legged on my chair at work, and wearing a summer clothing #help   \n",
       "\n",
       "                                                                                                                                                                                                                                                            Cleaned-tweet  \n",
       "2154                                                                                                                                                           omg this clothing be so motherfucking adorable and grunge loudly cry face sign of the horn light skin tone  \n",
       "8470                                                                                                                                                                    he do not look a thing like jesus but he talk like a gentleman like you remember when you be baby  \n",
       "4306                                                                                                                                                                                                               sea kraken win and that be hockey baby! . ! sea v bu f  \n",
       "4774   the future of italian industry be in the south where there be still unsaturated territory with qualified baby people increasingly orient towards business culture interview with president maurizio manfellotto on recovery plan and reform right arrow curve down  \n",
       "6774          i have a very clear and sensitive complexion. i have always use protection since i be a baby. i also use it in the city of turin. i would like to understand what the problem be with those who tell me but if you wear protection you will never get a tan  \n",
       "...                                                                                                                                                                                                                                                                   ...  \n",
       "2335                                                                                                             vegetable producer have launch three new category innovation for the freezer aisle restaurant fashion side zucchini tot and veggie spiral skillets. food  \n",
       "8554         for museo city create your map of milan with u a free interactive digital itinerary dedicate to baby between and year to get to know the place in the city link to the history of . we look forward to see you on saturday march at . left arrow curve right  \n",
       "11771                                                                                                                                                                                             silk at office fly split studio fly we heart pole silk behind the scene  \n",
       "5003                                                                                                                                                                                                wow the dick car dyke show season episode will you be my wife? f. via  \n",
       "8213                                                                                                                                    still have not master how to combine the fact that i like to sit cross legged on my chair at work and wear a summer clothing help  \n",
       "\n",
       "[12585 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mostra tweets pre-processati\n",
    "twitter_dataframe = pd.read_csv('dataframes/pre_processed_tweets.csv')\n",
    "all_user_list = list(np.unique(twitter_dataframe[\"User\"])) #lista di utenti\n",
    "print(\"loading CSV: \"+str(twitter_dataframe.shape[0])+\" tweet found [\",len(all_user_list),\"users]\")\n",
    "shuffle(twitter_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Sentence</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5275</th>\n",
       "      <td>MMyrick29</td>\n",
       "      <td>sea kraken win and that be hockey baby!</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13131</th>\n",
       "      <td>mollyrperk</td>\n",
       "      <td>get to meet and drink with giorgio at the office christmas party!</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>DolciTentation</td>\n",
       "      <td>biscuit with coffee cream.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13359</th>\n",
       "      <td>rmsothebys</td>\n",
       "      <td>the fast and most fearsome rally car be offer from the gran turismo collection this week.</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237</th>\n",
       "      <td>MC_G83</td>\n",
       "      <td>what you do not know about alhambra home aperitif un obvious beer clink beer mug.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11594</th>\n",
       "      <td>kaffeezentrale</td>\n",
       "      <td>an everyday life without coffee?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5906</th>\n",
       "      <td>MeritaSocial</td>\n",
       "      <td>watch live the italian economy in europe the challenge of convergence on astrid s youtube channel right arrow curve down.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10796</th>\n",
       "      <td>hasanaytekin</td>\n",
       "      <td>covid continue to push the car industry.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9349</th>\n",
       "      <td>calabriaonline</td>\n",
       "      <td>happy april forever italia italian.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8220</th>\n",
       "      <td>YleniaNea</td>\n",
       "      <td>on this gloomy friday at the end of november we really need a slice of vegan chocolate salami.</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15217 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 User  \\\n",
       "5275        MMyrick29   \n",
       "13131      mollyrperk   \n",
       "2325   DolciTentation   \n",
       "13359      rmsothebys   \n",
       "5237           MC_G83   \n",
       "...               ...   \n",
       "11594  kaffeezentrale   \n",
       "5906     MeritaSocial   \n",
       "10796    hasanaytekin   \n",
       "9349   calabriaonline   \n",
       "8220        YleniaNea   \n",
       "\n",
       "                                                                                                                        Sentence  \\\n",
       "5275                                                                                     sea kraken win and that be hockey baby!   \n",
       "13131                                                          get to meet and drink with giorgio at the office christmas party!   \n",
       "2325                                                                                                  biscuit with coffee cream.   \n",
       "13359                                  the fast and most fearsome rally car be offer from the gran turismo collection this week.   \n",
       "5237                                           what you do not know about alhambra home aperitif un obvious beer clink beer mug.   \n",
       "...                                                                                                                          ...   \n",
       "11594                                                                                           an everyday life without coffee?   \n",
       "5906   watch live the italian economy in europe the challenge of convergence on astrid s youtube channel right arrow curve down.   \n",
       "10796                                                                                   covid continue to push the car industry.   \n",
       "9349                                                                                         happy april forever italia italian.   \n",
       "8220                              on this gloomy friday at the end of november we really need a slice of vegan chocolate salami.   \n",
       "\n",
       "       Sentiment  \n",
       "5275           1  \n",
       "13131          1  \n",
       "2325           0  \n",
       "13359         -1  \n",
       "5237           0  \n",
       "...          ...  \n",
       "11594          0  \n",
       "5906           1  \n",
       "10796          0  \n",
       "9349           1  \n",
       "8220          -1  \n",
       "\n",
       "[15217 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mostra tweets con i valori di sentiment\n",
    "\n",
    "sentences = pd.read_excel('excel_results/sentences_vader.xlsx',index_col=0)\n",
    "#sentences = pd.read_excel('excel_results/sentences_textblob.xlsx',index_col=0)\n",
    "#sentences = pd.read_excel('excel_results/sentences_afinn.xlsx', index_col=0)\n",
    "#sentences = pd.read_excel('excel_results/sentences_spacytextblob.xlsx', index_col=0)\n",
    "#sentences = pd.read_excel('excel_results/sentences_flair.xlsx', index_col=0)\n",
    "#sentences = pd.read_excel('excel_results/sentences_sentistrength.xlsx', index_col=0)\n",
    "\n",
    "shuffle(sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Mostra la media dei sentimenti:\n",
    "\n",
    "sentences = pd.read_excel('excel_results/sentences_vader.xlsx',index_col=0)\n",
    "\n",
    "#Plot a histogram of frequencies\n",
    "df = pd.DataFrame(sentences[\"Sentiment\"].apply(print_value)).value_counts().plot(kind='barh')\n",
    "\n",
    "plt.title('Average sentiment of all sentences')\n",
    "plt.xlabel('Sentences')\n",
    "plt.savefig('histograms/sentences_sentiment.png', bbox_inches = 'tight')\n",
    "plt.show(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>baby</th>\n",
       "      <th>car</th>\n",
       "      <th>chocolate</th>\n",
       "      <th>clothing</th>\n",
       "      <th>coffee</th>\n",
       "      <th>computer</th>\n",
       "      <th>fashion</th>\n",
       "      <th>home</th>\n",
       "      <th>household appliances</th>\n",
       "      <th>italian</th>\n",
       "      <th>licorice</th>\n",
       "      <th>office</th>\n",
       "      <th>pasta</th>\n",
       "      <th>scent</th>\n",
       "      <th>sporty</th>\n",
       "      <th>technology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACE2276</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>pierocurcio</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>domasibarita</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>joelcontegen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>cathys78465636</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>giiuullyy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>wilhelmdonkor27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>legxndares</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>hlgmjs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>MichelaIntrieri</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>498 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                User  baby  car  chocolate  clothing  coffee  computer  \\\n",
       "3            ACE2276     0    1          0         0       0         0   \n",
       "427      pierocurcio    -1   -1          0         0       0         0   \n",
       "328     domasibarita     0    0          0         0       0         0   \n",
       "376     joelcontegen     0    0          0         0       0         0   \n",
       "315   cathys78465636     0    1          0         0       0         1   \n",
       "..               ...   ...  ...        ...       ...     ...       ...   \n",
       "347        giiuullyy     0    0          0         0       0         0   \n",
       "494  wilhelmdonkor27     0    0          0         0       0         0   \n",
       "389       legxndares     0    0          0         0       0         0   \n",
       "360           hlgmjs     0    0          0         0       0         0   \n",
       "180  MichelaIntrieri     0    0          0         1       0         0   \n",
       "\n",
       "     fashion  home  household appliances  italian  licorice  office  pasta  \\\n",
       "3          0     0                     0        0         0       0      0   \n",
       "427        0     0                     0        0         0       0      0   \n",
       "328        0     0                     0        0         0       0      0   \n",
       "376        0     0                     0        0         0       0      0   \n",
       "315        0     1                     0        0         0       0      0   \n",
       "..       ...   ...                   ...      ...       ...     ...    ...   \n",
       "347        0    -1                     0        0         0       0      0   \n",
       "494        0     0                     0        0         0       0      0   \n",
       "389        0     0                     0        0         0       0      0   \n",
       "360        0     1                     0        0         0       0      0   \n",
       "180        0     0                     0        0         0       0      0   \n",
       "\n",
       "     scent  sporty  technology  \n",
       "3        0       0           0  \n",
       "427      0       0           0  \n",
       "328      0       0           0  \n",
       "376      1       0           1  \n",
       "315      0       0           0  \n",
       "..     ...     ...         ...  \n",
       "347      0       0           0  \n",
       "494      0       0           0  \n",
       "389      0       0           0  \n",
       "360      0       0           0  \n",
       "180      0       0           0  \n",
       "\n",
       "[498 rows x 17 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Mostra la ta bella UTENTI-TAGS\n",
    "users_tags_table = pd.read_excel('excel_results/users_tags_table_vader.xlsx', index_col=0)\n",
    "#users_tags_table = pd.read_excel('excel_results/users_tags_table_textblob.xlsx', index_col=0)\n",
    "#users_tags_table = pd.read_excel('excel_results/users_tags_table_afinn.xlsx', index_col=0)\n",
    "#users_tags_table = pd.read_excel('excel_results/users_tags_table_spacytextblob.xlsx', index_col=0)\n",
    "#users_tags_table = pd.read_excel('excel_results/users_tags_table_flair.xlsx', index_col=0)\n",
    "#users_tags_table = pd.read_excel('excel_results/users_tags_table_sentistrength.xlsx', index_col=0)\n",
    "\n",
    "display(sklearn.utils.shuffle(users_tags_table))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_11832\\280927502.py:4: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared.\n",
      "  users_tags_table.hist(ax = ax)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x2000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize = (15,20))\n",
    "ax = fig.gca()\n",
    "users_tags_table.hist(ax = ax)\n",
    "plt.savefig('histograms/all_tags_sentiment.png', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>alfaromeo</th>\n",
       "      <th>samsung</th>\n",
       "      <th>lavazza</th>\n",
       "      <th>armani</th>\n",
       "      <th>adidas</th>\n",
       "      <th>microsoft</th>\n",
       "      <th>barilla</th>\n",
       "      <th>ferrero</th>\n",
       "      <th>delonghi</th>\n",
       "      <th>amarelli</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>GeorgeJohnson33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>ugcrosemary</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Emily_G_929</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ArchitRoma</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Foodex</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>alexmik28014658</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>Tino76347897</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>kalabrolio</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Ginvercillo</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>hbojunkie</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>498 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                User  alfaromeo  samsung  lavazza  armani  adidas  microsoft  \\\n",
       "107  GeorgeJohnson33          0        0        0       0       0          0   \n",
       "484      ugcrosemary          0        0        0       0       0          0   \n",
       "86       Emily_G_929         -1        0        0       1       1          0   \n",
       "19        ArchitRoma          0        0        0       0       0          0   \n",
       "92            Foodex          0        0        0       0       0          0   \n",
       "..               ...        ...      ...      ...     ...     ...        ...   \n",
       "285  alexmik28014658          0        0        0       0       0          0   \n",
       "254     Tino76347897          0        0        0       0       0          0   \n",
       "382       kalabrolio          0        0        0       0       0          0   \n",
       "112      Ginvercillo          0        0        0       0       0          0   \n",
       "356        hbojunkie          0        0        0       0       0          0   \n",
       "\n",
       "     barilla  ferrero  delonghi  amarelli  \n",
       "107        0        0         0         0  \n",
       "484        0        0         0         0  \n",
       "86         0        1         0         1  \n",
       "19         1        0         0         0  \n",
       "92         0        0         0         0  \n",
       "..       ...      ...       ...       ...  \n",
       "285        0        0         0         0  \n",
       "254        0        0         0         0  \n",
       "382        0        0         0         0  \n",
       "112        0        0         0         0  \n",
       "356        0        0         0         0  \n",
       "\n",
       "[498 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Mostra la ta bella UTENTI-TAGS\n",
    "users_brands_table = pd.read_excel('excel_results/users_brands_table_vader.xlsx', index_col=0)\n",
    "#users_brands_table = pd.read_excel('excel_results/users_brands_table_textblob.xlsx', index_col=0)\n",
    "#users_brands_table = pd.read_excel('excel_results/users_brands_table_afinn.xlsx', index_col=0)\n",
    "#users_brands_table = pd.read_excel('excel_results/users_brands_table_spacytextblob.xlsx', index_col=0)\n",
    "#users_brands_table = pd.read_excel('excel_results/users_brands_table_flair.xlsx', index_col=0)\n",
    "#users_brands_table = pd.read_excel('excel_results/users_brands_table_sentistrength.xlsx', index_col=0)\n",
    "\n",
    "display(sklearn.utils.shuffle(users_brands_table))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>alfaromeo</th>\n",
       "      <th>samsung</th>\n",
       "      <th>lavazza</th>\n",
       "      <th>armani</th>\n",
       "      <th>adidas</th>\n",
       "      <th>microsoft</th>\n",
       "      <th>barilla</th>\n",
       "      <th>ferrero</th>\n",
       "      <th>delonghi</th>\n",
       "      <th>amarelli</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21Phylk</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABO_mondo</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACE2276</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGnomblea</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>werewolfdonut</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>wilhelmdonkor27</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>wittystwitty</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>xkj1985x</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>498 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                User  alfaromeo   samsung   lavazza    armani    adidas  \\\n",
       "0           007maybe   0.833333  0.500000  0.500000  0.500000  0.666667   \n",
       "1            21Phylk   0.500000  0.500000  0.500000  0.833333  0.833333   \n",
       "2          ABO_mondo   0.833333  0.666667  0.666667  0.833333  0.666667   \n",
       "3            ACE2276   0.666667  0.500000  0.500000  0.500000  0.500000   \n",
       "4          AGnomblea   0.500000  0.500000  0.500000  0.500000  0.500000   \n",
       "..               ...        ...       ...       ...       ...       ...   \n",
       "493    werewolfdonut   0.500000  0.500000  0.500000  0.666667  0.666667   \n",
       "494  wilhelmdonkor27   0.500000  0.500000  0.500000  0.500000  0.500000   \n",
       "495     wittystwitty   0.500000  0.500000  0.500000  0.500000  0.500000   \n",
       "496         xkj1985x   0.666667  0.500000  0.333333  0.333333  0.666667   \n",
       "497     ziopaolo2015   0.500000  0.500000  0.500000  0.500000  0.500000   \n",
       "\n",
       "     microsoft   barilla   ferrero  delonghi  amarelli  \n",
       "0     0.500000  0.333333  0.666667  0.333333  0.666667  \n",
       "1     0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "2     0.666667  1.000000  0.666667  0.666667  0.500000  \n",
       "3     0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "4     0.500000  0.500000  0.666667  0.500000  0.666667  \n",
       "..         ...       ...       ...       ...       ...  \n",
       "493   0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "494   0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "495   0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "496   0.500000  0.333333  0.333333  0.500000  0.500000  \n",
       "497   0.500000  0.500000  0.500000  0.500000  0.500000  \n",
       "\n",
       "[498 rows x 11 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#USER -BRANDS CON RANGE {0, 0.5, 1}\n",
    "users_brands_df = pd.read_csv('final_results/users_brands_values.csv')\n",
    "users_brands_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Brand</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>alfaromeo</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>samsung</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>lavazza</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>armani</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>adidas</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4975</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4976</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>barilla</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4977</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>ferrero</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4978</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>delonghi</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4979</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>amarelli</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4980 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              User      Brand     index\n",
       "0         007maybe  alfaromeo  0.833333\n",
       "1         007maybe    samsung  0.500000\n",
       "2         007maybe    lavazza  0.500000\n",
       "3         007maybe     armani  0.500000\n",
       "4         007maybe     adidas  0.666667\n",
       "...            ...        ...       ...\n",
       "4975  ziopaolo2015  microsoft  0.500000\n",
       "4976  ziopaolo2015    barilla  0.500000\n",
       "4977  ziopaolo2015    ferrero  0.500000\n",
       "4978  ziopaolo2015   delonghi  0.500000\n",
       "4979  ziopaolo2015   amarelli  0.500000\n",
       "\n",
       "[4980 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PREDIZIONI:\n",
    "predictions_df =pd.read_csv(\"final_results/predictions_df.csv\")\n",
    "predictions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User</th>\n",
       "      <th>Brand</th>\n",
       "      <th>boolean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>alfaromeo</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>samsung</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>lavazza</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>armani</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>007maybe</td>\n",
       "      <td>adidas</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4975</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>microsoft</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4976</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>barilla</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4977</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>ferrero</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4978</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>delonghi</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4979</th>\n",
       "      <td>ziopaolo2015</td>\n",
       "      <td>amarelli</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4980 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              User      Brand  boolean\n",
       "0         007maybe  alfaromeo    False\n",
       "1         007maybe    samsung    False\n",
       "2         007maybe    lavazza    False\n",
       "3         007maybe     armani    False\n",
       "4         007maybe     adidas    False\n",
       "...            ...        ...      ...\n",
       "4975  ziopaolo2015  microsoft    False\n",
       "4976  ziopaolo2015    barilla    False\n",
       "4977  ziopaolo2015    ferrero    False\n",
       "4978  ziopaolo2015   delonghi    False\n",
       "4979  ziopaolo2015   amarelli     True\n",
       "\n",
       "[4980 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gold Standard\n",
    "gold_standard_df = pd.read_csv('final_results/gold_standard_df.csv')\n",
    "gold_standard_df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.8 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8 (tags/v3.10.8:aaaf517, Oct 11 2022, 16:50:30) [MSC v.1933 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "510bf11c66312c57553c778e95194a8e30cff11d9b6d05cf8b0c105636ab8e90"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}